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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 2025 COMP90042 Project"
      ],
      "metadata": {
        "id": "32yCsRUo8H33"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Readme\n",
        "\n",
        "##Code execution:\n",
        "\n",
        "Basically, you can run it in order from beginning to end.\n",
        "\n",
        "In the part of finding suitable claim-evidence pairs, we use a Sentence-BERT model and fine tune it, and use this model to embed evidence and claim. After finding the 1000 claims and evidences with the largest cosine similarity, we use the fine tuned cross embedding model to find the top k evidences and use them as input to the classification model.\n",
        "\n",
        "The downstream model uses a Transformer-based Cross-Encoder model for multi-classification tasks in automatic fact-checking system. We concatenate [claim_text] and [evidence_text] into a sequence, encode it through a Tokenizer, and feed the encoded input into the RoBERTa model. The model uses the [CLS] vector for semantic aggregation and is fine-tuned through a linear classification head to predict the four-class labels (SUPPORTS, REFUTES, NOT_ENOUGH_INFO, DISPUTED) of the claim-evidence correspondence.\n",
        "\n",
        "*If there is something to be noted for the marker, please mention here.*\n",
        "\n",
        "*If you are planning to implement a program with Object Oriented Programming style, please put those the bottom of this ipynb file*"
      ],
      "metadata": {
        "id": "XCybYoGz8YWQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.DataSet Processing"
      ],
      "metadata": {
        "id": "6po98qVA8bJD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load data and model from google drives"
      ],
      "metadata": {
        "id": "xR7-P0_0TWTR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjTWJEfzX99Z",
        "outputId": "6ffed1c8-6ead-48cf-d24d-f1cf5bb2f5fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer, InputExample, losses\n",
        "from torch.utils.data import DataLoader\n",
        "import torch\n",
        "import json\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "\n",
        "os.environ[\"WANDB_MODE\"] = \"disabled\"\n",
        "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
        "\n",
        "\n",
        "with open(\"train-claims.json\", \"r\") as f:\n",
        "    claims_data = json.load(f)\n",
        "\n",
        "with open(\"evidence.json\", \"r\") as f:\n",
        "    evidence_data = json.load(f)\n",
        "\n",
        "with open(\"test-claims-unlabelled.json\", \"r\") as f:\n",
        "    test_data = json.load(f)"
      ],
      "metadata": {
        "id": "qvpC2KX5_c67"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load dependencies\n",
        "\n",
        "Parameter settings\n",
        "*   MAX_LEN Maximum encoding length\n",
        "*   CHECKPOINT_PATH Model save path\n",
        "\n",
        "Dataset definition"
      ],
      "metadata": {
        "id": "K3llDuVATdmW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import RobertaTokenizer, RobertaModel\n",
        "import torch.nn as nn\n",
        "from torch.optim import AdamW\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from tqdm import tqdm\n",
        "\n",
        "MAX_LEN = 512\n",
        "CHECKPOINT_PATH = \"fact_check_checkpoint.pt\"\n",
        "\n",
        "import logging\n",
        "logging.basicConfig(filename='training_log.txt', level=logging.INFO)\n",
        "\n",
        "\n",
        "# 数据集定义\n",
        "class FactCheckDataset(Dataset):\n",
        "    def __init__(self, claims_path, evidence_path, tokenizer):\n",
        "        with open(claims_path, 'r', encoding=\"utf-8\") as f:\n",
        "            self.claims = json.load(f)\n",
        "        with open(evidence_path, 'r', encoding=\"utf-8\") as f:\n",
        "            self.evidences = json.load(f)\n",
        "\n",
        "        self.tokenizer = tokenizer\n",
        "        self.label_map = {\"SUPPORTS\": 0, \"REFUTES\": 1, \"NOT_ENOUGH_INFO\": 2, \"DISPUTED\": 3}\n",
        "        self.data = self._build_data()\n",
        "\n",
        "    def _build_data(self):\n",
        "        samples = []\n",
        "        for cid, item in self.claims.items():\n",
        "            claim_text = item[\"claim_text\"]\n",
        "            label = self.label_map[item[\"claim_label\"]]\n",
        "            evidence_texts = [self.evidences[eid] for eid in item[\"evidences\"] if eid in self.evidences]\n",
        "            evidence_concat = \" \".join(evidence_texts)[:2000]\n",
        "            samples.append((claim_text, evidence_concat, label))\n",
        "        return samples\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        claim, evidence, label = self.data[idx]\n",
        "        encoding = self.tokenizer(\n",
        "            claim,\n",
        "            evidence,\n",
        "            padding=\"max_length\",\n",
        "            truncation=True,\n",
        "            max_length=MAX_LEN,\n",
        "            return_tensors=\"pt\"\n",
        "        )\n",
        "        item = {key: val.squeeze(0) for key, val in encoding.items()}\n",
        "        item[\"labels\"] = torch.tensor(label)\n",
        "        return item"
      ],
      "metadata": {
        "id": "_i483BDoTc43"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Model Implementation"
      ],
      "metadata": {
        "id": "1FA2ao2l8hOg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Determine whether the model needs to be loaded"
      ],
      "metadata": {
        "id": "oQU6QwwYD5NP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "load_model = True"
      ],
      "metadata": {
        "id": "ETkr3QaBEBt0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Training model for embedding\n",
        "\n",
        "  - Using pretrained Bi-Encoder model to encode evidence quickly.\n",
        "  \n",
        "  - Using MultipleNegativesRankingLoss as loss function.\n",
        "  \n",
        "  - Create train examples(claim_text, evidence), evidences only contain positive ones.\n",
        "\n",
        "Input: train-claims.json\n",
        "\n",
        "Output: Embedding_model\n",
        "\n",
        "Trainning log is shown after this block"
      ],
      "metadata": {
        "id": "MvGRWdIWARfm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# download model or use the trained model according to 'load_model'\n",
        "if load_model:\n",
        "    model = SentenceTransformer(\"model/embedding-model\")\n",
        "else:\n",
        "  model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(f\"Using device: {device}\")\n",
        "\n",
        "\n",
        "# 构造训练数据\n",
        "train_examples = []\n",
        "for claim_id, claim_data in tqdm(claims_data.items()):\n",
        "    claim = claim_data['claim_text']\n",
        "    positive_evidences = [evidence_data[eid] for eid in claim_data['evidences']]\n",
        "\n",
        "    for ev in positive_evidences:\n",
        "        train_examples.append(InputExample(texts=[claim, ev], label=1.0))\n",
        "\n",
        "\n",
        "# DataLoader\n",
        "train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=32)\n",
        "\n",
        "# Loss：MultipleNegativesRankingLoss\n",
        "train_loss = losses.MultipleNegativesRankingLoss(model)\n",
        "\n",
        "# 开始训练\n",
        "print(\"Training\")\n",
        "model.fit(train_objectives=[(train_dataloader, train_loss)],\n",
        "          epochs=20,\n",
        "          warmup_steps=100)\n",
        "\n",
        "# 保存模型\n",
        "model.save(\"model/embedding-model\")"
      ],
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        },
        "outputId": "73905310-1d4e-421f-9656-c356f2cac631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
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          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
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          "output_type": "display_data",
          "data": {
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              "version_major": 2,
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          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
            "WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9476d658b0124b77a5d1facc14f0ebdc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1228/1228 [00:00<00:00, 113426.97it/s]\n",
            "/usr/local/lib/python3.11/dist-packages/datasets/table.py:1395: FutureWarning: promote has been superseded by promote_options='default'.\n",
            "  block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]\n",
            "/usr/local/lib/python3.11/dist-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by promote_options='default'.\n",
            "  table = cls._concat_blocks(blocks, axis=0)\n",
            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n",
            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2580' max='2580' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2580/2580 06:06, Epoch 20/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.690000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.328700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.234500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.182000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.166600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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    {
      "cell_type": "markdown",
      "source": [
        "2. Model for selecting the most relevant claims and evidence\n",
        "\n",
        "  - Using Cross-Encoder which is suitable for matching tasks requiring high precision\n",
        "  - Construct training data (positive and negative samples)\n",
        "\n",
        "Input: train-claims.json\n",
        "\n",
        "Output: model\n",
        "\n",
        "Trainning log is shown after this block"
      ],
      "metadata": {
        "id": "FvlDtA_cDkyL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import random\n",
        "from sentence_transformers import InputExample, CrossEncoder\n",
        "from torch.utils.data import DataLoader\n",
        "from tqdm import tqdm\n",
        "\n",
        "# 构建训练数据（正负样本）\n",
        "train_examples = []\n",
        "for claim_id, claim_info in tqdm(claims_data.items()):\n",
        "    claim_text = claim_info[\"claim_text\"]\n",
        "    positive_evidences = [evidence_data[eid] for eid in claim_info[\"evidences\"]]\n",
        "\n",
        "    for ev in positive_evidences:\n",
        "        train_examples.append(InputExample(texts=[claim_text, ev], label=1.0))  # 正样本\n",
        "\n",
        "    # 构造负样本\n",
        "    for _ in range(10):\n",
        "        neg_ev = random.choice(list(evidence_data.values()))\n",
        "        if neg_ev not in positive_evidences:\n",
        "            train_examples.append(InputExample(texts=[claim_text, neg_ev], label=0.0))\n",
        "\n",
        "# 构建 Dataloader\n",
        "train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=32)\n",
        "\n",
        "# 加载 Cross-Encoder 模型\n",
        "if load_model:\n",
        "    model = CrossEncoder(\"model/crossencoder-factcheck\")\n",
        "else:\n",
        "  model = CrossEncoder(\"cross-encoder/ms-marco-MiniLM-L-6-v2\", num_labels=1)\n",
        "\n",
        "# 训练模型\n",
        "model.fit(train_dataloader=train_dataloader, epochs=10, warmup_steps=100)\n",
        "\n",
        "# 保存模型\n",
        "model.save(\"model/crossencoder-factcheck\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789,
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        "id": "j_Vn0o6sDtEY",
        "outputId": "8e79e68d-e0c1-419b-b9e7-e7be5240568f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1228/1228 [10:15<00:00,  1.99it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/794 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "03e05dbff9304bf3aca6eba6acb40d37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
            "WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.33k [00:00<?, ?B/s]"
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              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
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            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/3.66k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/datasets/table.py:1395: FutureWarning: promote has been superseded by promote_options='default'.\n",
            "  block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]\n",
            "/usr/local/lib/python3.11/dist-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by promote_options='default'.\n",
            "  table = cls._concat_blocks(blocks, axis=0)\n",
            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n",
            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5130' max='5130' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5130/5130 09:18, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.108500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.009100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.004900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.002400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.001100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.000900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.000400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.000200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.Classification Model\n",
        "\n",
        "Model definition"
      ],
      "metadata": {
        "id": "nuQ4z7SMT5bZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 模型定义\n",
        "class CrossEncoderModel(nn.Module):\n",
        "    def __init__(self, model_name=\"roberta-base\", num_labels=4, class_weights=None):\n",
        "        super().__init__()\n",
        "        self.roberta = RobertaModel.from_pretrained(model_name)\n",
        "        self.classifier = nn.Linear(self.roberta.config.hidden_size, num_labels)\n",
        "        self.loss_fn = nn.CrossEntropyLoss(weight=class_weights)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask, labels=None):\n",
        "        outputs = self.roberta(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        cls_output = outputs.last_hidden_state[:, 0, :]\n",
        "        logits = self.classifier(cls_output)\n",
        "        if labels is not None:\n",
        "            loss = self.loss_fn(logits, labels)\n",
        "            return loss, logits\n",
        "        return logits\n",
        "\n",
        "# 混淆矩阵函数\n",
        "def plot_confusion_matrix(y_true, y_pred):\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    labels = [\"SUPPORTS\", \"REFUTES\", \"NOT_ENOUGH_INFO\", \"DISPUTED\"]\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=labels, yticklabels=labels)\n",
        "    plt.xlabel(\"Predicted\")\n",
        "    plt.ylabel(\"True\")\n",
        "    plt.title(\"Confusion Matrix\")\n",
        "    plt.show()\n",
        "\n",
        "def should_stop_early(losses, patience=3):\n",
        "    if len(losses) <= patience:\n",
        "        return False\n",
        "    recent = losses[-(patience + 1):]\n",
        "    return all(x >= recent[0] for x in recent[1:])"
      ],
      "metadata": {
        "id": "_BPUhhO7T5uL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training and evaluation functions"
      ],
      "metadata": {
        "id": "MwkjKFkWUEYH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 训练和评估\n",
        "def train_epoch(model, dataloader, optimizer, device):\n",
        "    model.train()\n",
        "    total_loss = 0\n",
        "    progress_bar = tqdm(dataloader, desc=\"Training\", leave=False)\n",
        "    for batch in progress_bar:\n",
        "        for k in batch:\n",
        "            batch[k] = batch[k].to(device)\n",
        "        loss, _ = model(**batch)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        total_loss += loss.item()\n",
        "        progress_bar.set_description(f\"Training (loss={loss.item():.4f})\")\n",
        "    avg_loss = total_loss / len(dataloader)\n",
        "    logging.info(f\" Epoch finished. Average loss: {avg_loss:.4f}\")\n",
        "    return avg_loss\n",
        "\n",
        "def evaluate(model, dataloader, device):\n",
        "    model.eval()\n",
        "    all_preds, all_labels = [], []\n",
        "    total_loss = 0\n",
        "    with torch.no_grad():\n",
        "        for batch in dataloader:\n",
        "            labels = batch[\"labels\"]\n",
        "            for k in batch:\n",
        "                batch[k] = batch[k].to(device)\n",
        "            output = model(**batch)\n",
        "            logits = output if isinstance(output, torch.Tensor) else output[1]\n",
        "            loss = output[0] if isinstance(output, tuple) else None\n",
        "            if loss is not None:\n",
        "                total_loss += loss.item()\n",
        "            preds = torch.argmax(logits, dim=1).cpu().numpy()\n",
        "            all_preds.extend(preds)\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "    logging.info(classification_report(all_labels, all_preds, target_names=[\"SUPPORTS\", \"REFUTES\", \"NOT_ENOUGH_INFO\", \"DISPUTED\"]))\n",
        "    # plot_confusion_matrix(all_labels, all_preds)\n",
        "    return total_loss / len(dataloader)"
      ],
      "metadata": {
        "id": "MekMMjRYUEwE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Start train\n",
        "\n",
        "*   model_mode Model mode (training or inference test)"
      ],
      "metadata": {
        "id": "Q0Zwbvo4UJ0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_mode = \"train\"  # \"train\" or \"test\""
      ],
      "metadata": {
        "id": "c4Q_GDy1ZHy7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 准备训练\n",
        "if model_mode == \"train\":\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\n",
        "\n",
        "    train_dataset = FactCheckDataset(\"train-claims.json\", \"evidence.json\", tokenizer)\n",
        "    dev_dataset = FactCheckDataset(\"dev-claims.json\", \"evidence.json\", tokenizer)\n",
        "\n",
        "    train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)\n",
        "    dev_loader = DataLoader(dev_dataset, batch_size=4)\n",
        "\n",
        "    train_labels = [label for _, _, label in train_dataset.data]\n",
        "    class_weights = compute_class_weight(class_weight=\"balanced\", classes=np.unique(train_labels), y=train_labels)\n",
        "    class_weights_tensor = torch.tensor(class_weights, dtype=torch.float).to(device)\n",
        "\n",
        "    model = CrossEncoderModel(class_weights=class_weights_tensor).to(device)\n",
        "    optimizer = AdamW(model.parameters(), lr=2e-5)\n",
        "\n",
        "    start_epoch = 0\n",
        "    if os.path.exists(CHECKPOINT_PATH):\n",
        "        logging.info(f\" Found checkpoint. Loading from {CHECKPOINT_PATH}\")\n",
        "        checkpoint = torch.load(CHECKPOINT_PATH, map_location=device)\n",
        "        model.load_state_dict(checkpoint[\"model_state_dict\"])\n",
        "        optimizer.load_state_dict(checkpoint[\"optimizer_state_dict\"])\n",
        "        start_epoch = checkpoint[\"epoch\"] + 1\n",
        "    else:\n",
        "        logging.info(\" No checkpoint found. Starting from scratch.\")\n",
        "\n",
        "    # 开始训练\n",
        "    EPOCHS = 10\n",
        "    patience = 3\n",
        "    eval_losses = []\n",
        "\n",
        "    best_dev_loss = float(\"inf\")\n",
        "    for epoch in range(start_epoch, EPOCHS):\n",
        "        logging.info(f\" Epoch {epoch+1}/{EPOCHS}\")\n",
        "        loss = train_epoch(model, train_loader, optimizer, device)\n",
        "        dev_loss = evaluate(model, dev_loader, device)\n",
        "\n",
        "        eval_losses.append(dev_loss)\n",
        "\n",
        "        # checkpoint\n",
        "        torch.save({\n",
        "            \"epoch\": epoch,\n",
        "            \"model_state_dict\": model.state_dict(),\n",
        "            \"optimizer_state_dict\": optimizer.state_dict(),\n",
        "        }, CHECKPOINT_PATH)\n",
        "        logging.info(f\" Checkpoint saved to {CHECKPOINT_PATH}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784,
          "referenced_widgets": [
            "7297655d596e44e4b16a4180a2a92e47",
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            "0e1b11c464864f91836ef6566f81f885",
            "b19efaa012dc46c4bf29cb2762673a88",
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        },
        "id": "qLg4p6pdUObp",
        "outputId": "6a6a84aa-65b9-4c75-ef97-10de979da2a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7297655d596e44e4b16a4180a2a92e47"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19b68624acd84d5a9b46ee0799a1fd3d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1fbd81454704c72b5f645a1788936d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b848c61c57064d12b1a60f0b5ee014a3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f7e213d7a0e4f91b7fe577bdf2f17ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
            "WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/499M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "447acce2ba08452992d0d31a9d8957b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-base and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Training (loss=1.4230):  75%|███████▍  | 229/307 [01:31<00:33,  2.35it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "Training (loss=1.3499):  97%|█████████▋| 297/307 [02:02<00:04,  2.43it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "Training (loss=1.6976):  32%|███▏      | 99/307 [00:41<01:25,  2.43it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n",
            "Training (loss=0.5023):  11%|█▏        | 35/307 [00:14<01:53,  2.40it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n",
            "Training (loss=0.4460):  89%|████████▉ | 274/307 [01:53<00:13,  2.41it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n",
            "Training (loss=0.4431):   4%|▍         | 13/307 [00:05<02:00,  2.44it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n",
            "Training (loss=0.0751):  73%|███████▎  | 225/307 [01:33<00:33,  2.44it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n",
            "Training (loss=0.5351):  46%|████▌     | 141/307 [00:58<01:07,  2.45it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n",
            "Training (loss=0.0119):  12%|█▏        | 37/307 [00:15<01:53,  2.38it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n",
            "Training (loss=0.0080):   1%|▏         | 4/307 [00:01<02:04,  2.44it/s]Be aware, overflowing tokens are not returned for the setting you have chosen, i.e. sequence pairs with the 'longest_first' truncation strategy. So the returned list will always be empty even if some tokens have been removed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.Testing and Evaluation"
      ],
      "metadata": {
        "id": "EzGuzHPE87Ya"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Embed the evidence to facilitate the subsequent model call"
      ],
      "metadata": {
        "id": "NqkSqievBwI4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import torch\n",
        "from sentence_transformers import SentenceTransformer, util\n",
        "from tqdm import tqdm\n",
        "\n",
        "# 读取数据\n",
        "with open(\"train-claims.json\", \"r\") as f:\n",
        "    claims_data = json.load(f)\n",
        "\n",
        "with open(\"evidence.json\", \"r\") as f:\n",
        "    evidence_data = json.load(f)\n",
        "\n",
        "# 加载模型\n",
        "model = SentenceTransformer(\"model/embedding-model\")\n",
        "\n",
        "# 所有 evidence 列表及其 ID\n",
        "all_evidence_ids = list(evidence_data.keys())\n",
        "all_evidence_texts = [evidence_data[eid] for eid in all_evidence_ids]\n",
        "\n",
        "# 预编码所有 evidence 向量，加快速度\n",
        "print(\"Encoding all evidences...\")\n",
        "\n",
        "all_evidence_embeddings = model.encode(all_evidence_texts, convert_to_tensor=True)\n",
        "torch.save(all_evidence_embeddings, \"output/evidences_embedding.pt\")"
      ],
      "metadata": {
        "id": "6ZVeNYIH9IaL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60762dd3-45a4-4316-c1bb-102ced0c5a10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encoding all evidences...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "input: claim_text\n",
        "\n",
        "output: claim_text + pridict_evidence"
      ],
      "metadata": {
        "id": "Rj31nM54B8yF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import torch\n",
        "from sentence_transformers import CrossEncoder, util, SentenceTransformer\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "model = CrossEncoder(\"model/crossencoder-factcheck\")\n",
        "embedding_model = SentenceTransformer(\"model/embedding-model\")\n",
        "\n",
        "# 所有 evidence 列表及其 ID\n",
        "all_evidence_ids = list(evidence_data.keys())\n",
        "all_evidence_texts = [evidence_data[eid] for eid in all_evidence_ids]\n",
        "all_evidence_embeddings = torch.load(\"output/evidences_embedding.pt\")\n",
        "\n",
        "results = {}\n",
        "\n",
        "for claim_id, claim_info in tqdm(test_data.items()):\n",
        "    claim_text = claim_info['claim_text']\n",
        "\n",
        "    # 编码 claim\n",
        "    claim_embedding = embedding_model.encode(claim_text, convert_to_tensor=True)\n",
        "\n",
        "    # 计算与所有 evidence 的相似度\n",
        "    cosine_scores = util.cos_sim(claim_embedding, all_evidence_embeddings)\n",
        "\n",
        "    # 获取 top-1000 相关 evidence\n",
        "    top_k = 1000\n",
        "    top_results = torch.topk(cosine_scores[0], k=top_k)\n",
        "\n",
        "    possible_evidence_ids = [all_evidence_ids[idx] for idx in top_results.indices]\n",
        "    possible_evidence_texts = [all_evidence_texts[idx] for idx in top_results.indices]\n",
        "\n",
        "\n",
        "    top_k = 4\n",
        "\n",
        "    scores = model.predict([[claim_text, evidence] for evidence in possible_evidence_texts])\n",
        "    top_evidence_ids = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:top_k]\n",
        "    top_evidence_context = [possible_evidence_ids[i] for i in top_evidence_ids]\n",
        "\n",
        "    # 保存结果\n",
        "    results[claim_id] = {\n",
        "        \"claim_text\": claim_text,\n",
        "        \"evidences\": top_evidence_context,\n",
        "    }\n",
        "\n",
        "with open(\"test-claims+evidence.json\", \"w\") as f:\n",
        "    json.dump(results, f)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wkQIx2DB-ee",
        "outputId": "f95e3792-6495-478a-ab26-e79f849ad26e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 153/153 [02:29<00:00,  1.03it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inference and output test set"
      ],
      "metadata": {
        "id": "Z4n7RsmmB8-7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 输出预测文件\n",
        "def predict_and_save_labels(model, claims_path, evidence_path, output_path, tokenizer, batch_size=4):\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    model.to(device)\n",
        "\n",
        "    with open(claims_path, 'r', encoding=\"utf-8\") as f:\n",
        "        input_claims = json.load(f)\n",
        "    with open(evidence_path, 'r', encoding=\"utf-8\") as f:\n",
        "        evidences = json.load(f)\n",
        "\n",
        "    label_map_rev = {0: \"SUPPORTS\", 1: \"REFUTES\", 2: \"NOT_ENOUGH_INFO\", 3: \"DISPUTED\"}\n",
        "\n",
        "    output_results = {}\n",
        "\n",
        "    for cid, item in tqdm(input_claims.items(), desc=\"Predicting\"):\n",
        "        claim_text = item[\"claim_text\"]\n",
        "        evidence_texts = [evidences[eid] for eid in item[\"evidences\"] if eid in evidences]\n",
        "        evidence_concat = \" \".join(evidence_texts)[:2000]\n",
        "\n",
        "        encoding = tokenizer(\n",
        "            claim_text,\n",
        "            evidence_concat,\n",
        "            padding=\"max_length\",\n",
        "            truncation=True,\n",
        "            max_length=MAX_LEN,\n",
        "            return_tensors=\"pt\"\n",
        "        )\n",
        "\n",
        "        encoding = {k: v.to(device) for k, v in encoding.items()}\n",
        "        with torch.no_grad():\n",
        "            logits = model(**encoding)\n",
        "            logits = logits if isinstance(logits, torch.Tensor) else logits[1]\n",
        "            pred_label = torch.argmax(logits, dim=1).item()\n",
        "            label_str = label_map_rev[pred_label]\n",
        "\n",
        "        output_results[cid] = {\n",
        "            \"claim_text\": claim_text,\n",
        "            \"claim_label\": label_str,\n",
        "            \"evidences\": item[\"evidences\"]\n",
        "        }\n",
        "\n",
        "    with open(output_path, 'w', encoding=\"utf-8\") as f_out:\n",
        "        json.dump(output_results, f_out)\n",
        "\n",
        "    print(f\"Inference completed, results saved to: {output_path}\")"
      ],
      "metadata": {
        "id": "jfUNSMEsUV7O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Start inference test"
      ],
      "metadata": {
        "id": "8xyuv_wXUc9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_mode = \"test\"  # \"train\" or \"test\""
      ],
      "metadata": {
        "id": "-N98DSmNfM9t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if model_mode == \"test\":\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\n",
        "    model = CrossEncoderModel()\n",
        "    state_dict = torch.load(CHECKPOINT_PATH, map_location=device)[\"model_state_dict\"]\n",
        "    model.load_state_dict({k: v for k, v in state_dict.items() if k in model.state_dict()}, strict=False)\n",
        "    model.eval()\n",
        "\n",
        "    predict_and_save_labels(\n",
        "        model=model,\n",
        "        claims_path=\"test-claims+evidence.json\",\n",
        "        evidence_path=\"evidence.json\",\n",
        "        output_path=\"test-output.json\",\n",
        "        tokenizer=tokenizer\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JJ4jv1OUbwj",
        "outputId": "89b57df4-6327-46c2-c72d-3b19ca15d3e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-base and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Predicting: 100%|██████████| 153/153 [00:05<00:00, 28.69it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "推理完成，结果已保存至: test-output.json\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Object Oriented Programming codes here\n",
        "\n",
        "*You can use multiple code snippets. Just add more if needed*"
      ],
      "metadata": {
        "id": "mefSOe8eTmGP"
      }
    }
  ]
}